By integrating sociological factors, this study explores novel causal mechanisms to broaden the understanding of subjective economic well-being (SEWB). The results of a probit regression analysis of Indian Human Development Survey (IHDS) cross-sectional and panel data reveal a positive, significant effect of successful upward intergenerational occupational mobility on improved perceptions of SEWB. The findings remain robust and significant irrespective of changes in the acquisition and possession of household material assets. The empirical evidence presented herein showcases the utility of adding sociological factors to the discourse on SEWB. Additionally, the present study offers an alternative perspective on the primacy accorded to material possessions in the SEWB literature in marketing. The functionings-capabilities framework is offered as an explanatory framework to interpret the findings. Together, the study offers clear theoretical and managerial contributions to a more nuanced understanding of SEWB.

Researchers and practitioners have been interested in solving real-world problems through clustering. The clustering of nodes in networks with unipartite or bipartite structure is important to explore real-world complex networks present in nature and society. Bipartite networks form an important class of complex networks because they reveal the heterogeneity of nodes in a network. However, most extant clustering methods focus only on unipartite networks. In this work, a novel constrained agglomerative clustering method applicable to unipartite and bipartite networks has been proposed. Initially, the topology of a network is modeled according to set-theoretic principles. Subsequently, the concepts related to rough set theory and relative linkage are used to cluster the set of nodes. The utility and effectiveness of the proposed approach are demonstrated through offline experiments on unipartite and bipartite networks. A comparison against ten state-of-the-art similarity measures over two different partitional clustering algorithms reveals the effectiveness of the proposed relative linkage measure. Moreover, a comparative analysis with state-of-the-art network clustering methods reveals the viability of the proposed rough set-based constrained agglomerative clustering algorithm. Finally, the proposed method has been applied for the detection of cohesive subgroups of banks in a real bipartite network formed by mapping credit relationships between Indian firms and banks. (C) 2020 Elsevier Inc. All rights reserved.

Intensive care unit (ICU) is a critical resource in a hospital, especially in developing countries such as India. The length of ICU stay after a cardiac surgery is an important variable for effective use of this critical resource. In this context, a predictive model can help a hospital to make optimum use of its ICU occupancy. A study was thus conducted on ICU patients and data gather over a 1-year period in a hospital in India. The critical factors for prolonged ICU stay (more than 72 h) were identified using univariate and multivariate logistic regression and a predictive index was built based on model development set. The predictive index was tested on a validation set and the mean length of ICU stay appeared to increase with an increase in the risk score. In addition, the risk score was tested in case of mortality. Efficient use of the ICU facility is possible with the help of this predictive index.

Dynamic pricing with automation and option to sell in-house-generated renewable energy to the grid attract consumers to the future retail electricity market. Real-time pricing of electricity in the Indian context has not been relevant so far because the pricing in Indian urban retail electricity market is mostly restricted to block-rate pricing. This study describes a novel method for segmenting the retail electricity market in a university setup. The experiment was focused on differential pricing, and the data were collected from 173 respondents using a questionnaire. This study prescribes different criteria for classifying a consumer into the specified market segments using cluster analysis and discriminant analysis. The demand-price relationship and price elasticity are studied for each segment. Demand characteristics of four appliances are also discussed. The study found that in some cases, consumers' willingness to pay is about five times higher than the present average price.

Achieving timely last-mile order delivery is often the most challenging part of an e-commerce order fulfillment. Effective management of last-mile operations can result in significant cost savings and lead to increased customer satisfaction. Currently, due to the lack of customer availability information, the schedules followed by delivery agents are optimized for the shortest tour distance. Therefore, orders are not delivered in customer-preferred time periods resulting in missed deliveries. Missed deliveries are undesirable since they incur additional costs. In this paper, we propose a decision support framework that is intended to improve delivery success rates while reducing delivery costs. Our framework generates delivery schedules by predicting the appropriate delivery time periods for order delivery. In particular, the proposed framework works in two stages. In the first stage, order delivery success for every order throughout the delivery shift is predicted using machine learning models. The predictions are used as an input for the optimization scheme, which generates delivery schedules in the second stage. The proposed framework is evaluated on two real-world datasets collected from a large e-commerce platform. The results indicate the effectiveness of the decision support framework in enabling savings of up to 10.2% in delivery costs when compared to the current industry practice.

In this study, we systematically review the literature on adoption factors and impacts of labor-saving technologies (LSTs) by smallholder and women farmers in developing countries. 85 articles are included in the review after meeting strict selection criteria through a search across several electronic platforms. We highlight several research gaps that need future research focus. Future research should include gendered differences in factors such as-comparing extension models, social networks, and farmers' underlying technological perceptions. We show the need for designing and providing access to gender-friendly LSTs suited to the context. While there are clear impacts of LST adoption on labor and productivity, few studies examine negative consequences such as labor-displacement. Further examination of these trade-offs and differential impacts on welfare dimensions across gender is needed. Our results indicate implications for future research and policy regarding incorporating gender differences in designing, promotion, and adoption of LSTs to reduce womnen's work burdens and to enhance welfare outcomes.

In this paper, we study the hub location problem of an entrant airline that tries to maximize its share in a market with already existing competing players. The problem is modeled as a non-linear integer program, which is intractable for off-the-shelf commercial solvers, like CPLEX and Gurobi, etc. Hence, we propose four alternate approaches to solve the problem. The first among them uses the Kelley's cutting plane method, the second is based on a mixed integer second order conic program reformulation, the third uses the Kelley's cutting plane method within Lagrangian relaxation, while the fourth uses second order conic program within Lagrangian relaxation. On the basis of extensive numerical tests on well-known datasets (CAB and AP), we conclude that the Kelley's cutting plane within Lagrangian relaxation is computationally the best. It is able to solve all the problem instances of upto 50 nodes within 1% optimality gap in less than 10 minutes of CPU time. (C) 2020 Elsevier B.V. All rights reserved.

Purpose In the context of sharing economy, the superhost program of Airbnb emerges as a phenomenal success story that has transformed the tourism industry and garnered humongous popularity. Proper performance evaluation and classification of the superhosts are crucial to incentivize superhosts to maintain higher service quality. The main objective of this paper is to design an integrated multicriteria decision-making (MCDM) method-based performance evaluation and classification framework for the superhosts of Airbnb and to study the variation in various contextual factors such as price, number of listings and cancelation policy across the superhosts. Design/methodology/approach This work considers three weighting techniques, mean, entropy and CRITIC-based methods to determine the weights of factors. For each of the weighting techniques, an integrated TOPSIS-MOORA-based performance evaluation method and classification framework have been developed. The proposed methodology has been applied for the performance evaluation of the superhosts (7,308) of New York City using real data from Airbnb. Findings From the perspective of performance evaluation, the importance of devising an integrated methodology instead of adopting a single approach has been highlighted using a nonparametric Wilcoxon signed-rank test. As per the context-specific findings, it has been observed that the price and the number of listings are the highest for the superhosts in the topmost category. Practical implications The proposed methodology facilitates the design of a leaderboard to motivate service providers to perform better. Also, it can be applicable in other accommodation-sharing economy platforms and ride-sharing platforms. Originality/value This is the first work that proposes a performance evaluation and classification framework for the service providers of the sharing economy in the context of tourism industry.

The content generation strategy of a sports franchise determines whether the user engagement increases or decreases on social media platforms. Thus, the role of Chief Operating Officer (COO) is profound who generally decides and governs social media policies of the franchises. We show that the cultural differences between local-COO vis-a-vis foreign-COO-governed sports franchises reflect in their content generation strategy and are also associated with user engagement. We use Hofstede's cultural dimensions theory and extract relevant features from the tweets. Overall, the results show that user engagement is more when the content generation strategy is in alignment with fans' national culture. The first contribution of our work is towards showing the incremental impact of power distance, individualism and collectivism on user engagement. The second contribution of our work is towards feature construction, feature selection and building authorship attribution classifiers to understand the content generation strategy. Prior literature shows that national culture impacts writing of online reviews. We investigate the role of national culture in social media content generation and user engagement and extend the literature. Our study is useful for organizations to understand the role of national culture in content generation and how it is related to user engagement.

Purpose The purpose of this paper is to test relevance of the information quality (IQ) framework in understanding quality of advertisements (ads) posted by ordinary consumers. Design/methodology/approach The main objective of this study is to assess quality ads posted on customer-to-customer (C2C) social commerce platforms from an IQ framework. The authors deployed innovative text mining techniques to generate features from the IQ framework and then used a machine learning (ML) algorithm to classify ads into three categories - high quality, medium quality and low quality. Findings The results show that not all dimensions of IQ framework are important to assess quality of ads posted on the platforms. Potential buyers on these platforms look for appropriate amount of information, which is objective, concise and complete, to make a potential purchase decision. Research limitations/implications As the research focuses on specific product categories, it lacks generalisability. Therefore, it needs to be tested for other product categories. Practical implications The paper includes recommendation for C2C marketplaces on how to increase quality of ads posted by consumers on the platform. Originality/value This study has focused on the user-generated content posted by ordinary consumers on the C2C commerce platform to sell used goods. Though C2C model has been developed on ads posted on C2C platforms, it can be established for brands as it provides them with an insight into latent dimensions that a consumer shall look for in an ad on social commerce platforms.

Introduction: The time from symptom onset to arrival at healthcare facility, and door to reperfusion time in treatment of acute coronary syndrome (ACS) can be improved significantly if the patient or the relatives can record a 12-lead ECG at home and transmit it to the physician for prompt interpretation. To make this widely applicable, the 12-lead ECG recording device has to be simple and user friendly. In this regard, torso ECG (T-ECG) electrode positions that are less cumbersome than the conventional ECG (C-ECG) electrode positions are an alternative worthy of consideration. Objective: and setting: To study the utility of T-ECG versus C-ECG in ACS patients. Design: and intervention: We proposed torso electrode positions in which upper limb electrodes were placed in the respective deltopectoral grooves below the lateral end of the clavicle; the right lower limb electrode was placed 2 finger breadths above the umbilicus and the left lower limb electrode, 2 finger breadths to the left of the umbilicus. We then studied the ECGs recorded, to ascertain whether T-ECGs miss or over-diagnose ACS changes. Twelve lead ECGs were recorded by both techniques (C-ECG & TECG) in 1361 patientsfrom the coronary care unit & out-patient department of a tertiary care hospital. A total of 1526 sets of ECGs (each set consisting of one C-ECG and one T -ECG) were read by two trained cardiologists independently and in a blinded fashion. There were 457 ECG sets from 342 patients with ACS. Of these, 116 ECG sets from 112 patients of anterior infarction who had changes restricted to precordial leads were excluded. Finally, 341 ECG sets from 230 patients with ACS and 324 sets of patients diagnosed to be normal on C-ECG were considered for the purpose of this study. Main results: All 341 ECG sets from the 230 patients of ACS diagnosed by C-ECG were correctly diagnosed by T-ECG (100% sensitivity) and all 324 normal ECGs on C-ECG were also identified as normal on T-ECG (100% specificity). Of the ACS ECGs, ST elevation was seen in 234 ECGs and ST depressions 154 ECGs. The localizations of ST elevation and ST depression were also accurately diagnosed by the T-ECG. Conclusion: The ECG recorded by our novel proposed torso electrode positions is comparable to a conventional ECG for the diagnosis of ACS. (C) 2021 Cardiological Society of India. Published by Elsevier B.V.

Government Owned Banks (GOBs) have other explicit or implicit objectives apart from profit maximization. In this paper, I study whether this affects the liquidation risk of firms borrowing from GOBs. Using the natural experiment of securitization reform in India that increased firms' liquidation risk, I find that the firms borrowing exclusively from GOBs did less reduction in secured debt usage compared to other firms. In the cross-section, the effect is more substantial in the subsample of firms that are more likely to face financial distress. These results suggest that borrowing from GOBs have less liquidation risk.

We study the problem of assigning doctors to existing, non-operational Primary Health Centers (PHCs). We do this in the presence of clear guidelines on the maximum population that can be served by any PHC, and uncertainties in the availability of the doctors over the planning horizon. We model the problem as a robust capacitated multi-period maximal covering location problem with server uncertainty. Such supply-side uncertainties have not been accounted for in the context of multi-period facility location in the extant literature. We present an MIP formulation of this problem, which turns out to be too difficult for an off-the-shelf solver like CPLEX. We, therefore, present several dominance rules to reduce the size of the model. We further propose a Benders decomposition based solution method with several refinements that exploit the underlying structure of the problem to solve it extremely efficiently. Our computational experiments show one of the variants of our Benders decomposition based method to be on average almost 100 0 times faster, compared to the CPLEX MIP solver, for problem instances containing 300 demand nodes and 10 facilities. Further, while the CPLEX MIP solver could not solve most of the instances beyond 300 demand nodes and 10 facilities even after 20 hours, two of our variants of Benders decomposition could solve instances upto the size of 500 demand nodes and 15 facilities in less than 0.5 hour, on average. (C) 2020 Elsevier B.V. All rights reserved.